Dataset Information

Treating Tobacco Dependence at the Intersection of Diversity, Poverty, and Mental Illness: A Randomized Feasibility and Replication Trial.

ABSTRACT: In an ethnically-diverse, uninsured psychiatric sample with co-occurring drug/alcohol addiction, we evaluated the feasibility and reproducibility of a tobacco treatment intervention. The intervention previously demonstrated efficacy in insured psychiatric and nonpsychiatric samples with 20.0%-25.0% abstinence at 18 months.Daily smokers, recruited in 2009-2010 from psychiatric units at an urban public hospital, were randomized to usual care (on-unit nicotine replacement plus quit advice) or intervention, which added a Transtheoretical-model tailored, computer-assisted intervention, stage-matched manual, brief counseling, and 10-week post-hospitalization nicotine replacement.The sample (N = 100, 69% recruitment rate, age M = 40) was 56% racial/ethnic minority, 65% male, 79% unemployed, and 48% unstably housed, diagnosed with unipolar (54%) and bipolar (14%) depression and psychotic disorders (46%); 77% reported past-month illicit drug use. Prior to hospitalization, participants averaged 19 (SD = 11) cigarettes/day for 23 (SD = 13) years; 80% smoked within 30 minutes of awakening; 25% were preparing to quit. Encouraging and comparable to effects in the general population, 7-day point prevalence abstinence for intervention versus control was 12.5% versus 7.3% at 3 months, 17.5% versus 8.5% at 6 months, and 26.2% versus 16.7% at 12 months. Retention exceeded 80% over 12 months. The odds of abstinence increased over time, predicted by higher self-efficacy, greater perceived social status, and diagnosis of psychotic disorder compared to unipolar depression.Findings indicate uninsured smokers with serious mental illness can engage in tobacco treatment research with quit rates comparable to the general population. A larger investigation is warranted. Inclusion of diverse smokers with mental illness in clinical trials is supported and encouraged.

SUBMITTER: Hickman NJ

PROVIDER: S-EPMC4580546 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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Treating Tobacco Dependence at the Intersection of Diversity, Poverty, and Mental Illness: A Randomized Feasibility and Replication Trial.

Hickman Norval J NJ Delucchi Kevin L KL Prochaska Judith J JJ

Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco 20150801 8

<h4>Introduction</h4>In an ethnically-diverse, uninsured psychiatric sample with co-occurring drug/alcohol addiction, we evaluated the feasibility and reproducibility of a tobacco treatment intervention. The intervention previously demonstrated efficacy in insured psychiatric and nonpsychiatric samples with 20.0%-25.0% abstinence at 18 months.<h4>Methods</h4>Daily smokers, recruited in 2009-2010 from psychiatric units at an urban public hospital, were randomized to usual care (on-unit nicotine r ...[more]

PMID: 26180227

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Project description:BackgroundOxford Mental Illness and Suicide tool (OxMIS) is a brief, scalable, freely available, structured risk assessment tool to assess suicide risk in patients with severe mental illness (schizophrenia-spectrum disorders or bipolar disorder). OxMIS requires further external validation, but a lack of large-scale cohorts with relevant variables makes this challenging. Electronic health records provide possible data sources for external validation of risk prediction tools. However, they contain large amounts of information within free-text that is not readily extractable. In this study, we examined the feasibility of identifying suicide predictors needed to validate OxMIS in routinely collected electronic health records.MethodsIn study 1, we manually reviewed electronic health records of 57 patients with severe mental illness to calculate OxMIS risk scores. In study 2, we examined the feasibility of using natural language processing to scale up this process. We used anonymized free-text documents from the Clinical Record Interactive Search database to train a named entity recognition model, a machine learning technique which recognizes concepts in free-text. The model identified eight concepts relevant for suicide risk assessment: medication (antidepressant/antipsychotic treatment), violence, education, self-harm, benefits receipt, drug/alcohol use disorder, suicide, and psychiatric admission. We assessed model performance in terms of precision (similar to positive predictive value), recall (similar to sensitivity) and F1 statistic (an overall performance measure).ResultsIn study 1, we estimated suicide risk for all patients using the OxMIS calculator, giving a range of 12 month risk estimates from 0.1-3.4%. For 13 out of 17 predictors, there was no missing information in electronic health records. For the remaining 4 predictors missingness ranged from 7-26%; to account for these missing variables, it was possible for OxMIS to estimate suicide risk using a range of scores. In study 2, the named entity recognition model had an overall precision of 0.77, recall of 0.90 and F1 score of 0.83. The concept with the best precision and recall was medication (precision 0.84, recall 0.96) and the weakest were suicide (precision 0.37), and drug/alcohol use disorder (recall 0.61).ConclusionsIt is feasible to estimate suicide risk with the OxMIS tool using predictors identified in routine clinical records. Predictors could be extracted using natural language processing. However, electronic health records differ from other data sources, particularly for family history variables, which creates methodological challenges.

Project description:BackgroundIndividuals with serious mental illnesses experience deaths related to smoking at a higher prevalence than individuals without a psychotic-spectrum disorders. Traditional smoking cessation programs are often not effective among individuals with chronic mental disorders. Little is known about how to implement a tobacco cessation treatment program for this at-risk population within a community health center. The current study used qualitative methods to examine the factors that may enhance or impede the delivery of a novel tobacco cessation treatment for smokers with a psychotic-spectrum disorder diagnosis in an integrated care community health center.MethodsUsing a case study design, we conducted 22 semi-structured interviews with primary care providers, mental health providers, addiction counselors, case managers, intake specialists, schedulers, pharmacists, and administrative staff employed at the organization. Interviews were transcribed and themes were identified through a rich coding process.ResultsWe identified environmental factors, organizational factors, provider factors and patient factors which describe the potential factors that may enhance or impede the implementation of a smoking cessation program at the integrated care community health center. Most notably, we identified that community mental health centers looking to implement a smoking cessation program for individuals with chronic mental health disorders should ensure the incentives for providers to participate align with the program's objectives. Additionally, organizations should invest in educating providers to address stigma related to smoking cessation and nicotine use.ConclusionsThe findings of our study provide valuable insight for administrators to consider when implementing a smoking cessation program in an integrated care community health center. Our findings provide public health practitioners with potential considerations that should be discussed when designing and implementing a smoking cessation program for individuals with chronic mental disorders.

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Treating Tobacco Dependence at the Intersection of Diversity, Poverty, and Mental Illness: A Randomized Feasibility and Replication Trial.

Publications

Treating Tobacco Dependence at the Intersection of Diversity, Poverty, and Mental Illness: A Randomized Feasibility and Replication Trial.

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